The complexity of vehicle E/E-architectures grows with increasing vehicle functions and driver assistance systems. I. e., environment recognition systems rely on lidar, radar, and camera sensors, which each are subject to multiple complex failure modes. Faults within a component may propagate into further systems, due to functions. On top, the number of system variants, both in soft- and hardware, increases. Subsequently, challenges arise for the development of complete and accurate diagnostic concepts starting with the identification of faults and their effect on a system as a whole (FMEA scope). This paper presents a methodology to automate the Failure Modes and Effects Analysis process using a graph representation of a mechatronic system. The representation, a causal-structural dependency graph, is automatically generated by exploiting existing standardized data sources (such as SysML and ODX) and its analysis is capable to identify potential failure modes and their associated effects. The graph model allows for an efficient analysis of complex vehicle systems, and provides a clear and intuitive representation of the system's structure and behavior. The proposed method is validated using a case study of an environment-recognition system. Results demonstrate its effectiveness in identifying potential failure modes and their effects. It has the potential to significantly reduce time and effort required for FMEA, and improve accuracy and completeness of a diagnostic concept.
Session: TESTING II | | 13:30 - 14:00